Vision

VisionDataModule

Bases: LightningDataModule, DataModule[BatchType_co]

A LightningDataModule for image datasets.

(Taken from pl_bolts which is not very well maintained.)

name class-attribute

name: str = ''

Dataset name.

dataset_cls class-attribute

dataset_cls: type[VisionDataset]

Dataset class to use.

dims class-attribute

dims: tuple[C, H, W]

A tuple describing the shape of the data.

__init__

__init__(
    data_dir: str | Path = DATA_DIR,
    val_split: int | float = 0.2,
    num_workers: int = NUM_WORKERS,
    normalize: bool = False,
    batch_size: int = 32,
    seed: int = 42,
    shuffle: bool = True,
    pin_memory: bool = True,
    drop_last: bool = False,
    train_transforms: Callable | None = None,
    val_transforms: Callable | None = None,
    test_transforms: Callable | None = None,
    **kwargs
) -> None

Parameters:

Name	Type	Description	Default
data_dir	str | Path	Where to save/load the data	DATA_DIR
val_split	int | float	Percent (float) or number (int) of samples to use for the validation split	0.2
num_workers	int	How many workers to use for loading data	NUM_WORKERS
normalize	bool	If true applies image normalize	False
batch_size	int	How many samples per batch to load	32
seed	int	Random seed to be used for train/val/test splits	42
shuffle	bool	If true shuffles the train data every epoch	True
pin_memory	bool	If true, the data loader will copy Tensors into CUDA pinned memory before returning them	True
drop_last	bool	If true drops the last incomplete batch	False
train_transforms	Callable | None	transformations you can apply to train dataset	None
val_transforms	Callable | None	transformations you can apply to validation dataset	None
test_transforms	Callable | None	transformations you can apply to test dataset	None

prepare_data

prepare_data() -> None

Saves files to data_dir.

default_transforms abstractmethod

default_transforms() -> Callable

Default transform for the dataset.

train_dataloader

train_dataloader(
    _dataloader_fn: Callable[
        Concatenate[Dataset, P], DataLoader
    ] = DataLoader,
    *args: args,
    **kwargs: kwargs
) -> DataLoader

The train dataloader.

val_dataloader

val_dataloader(
    _dataloader_fn: Callable[
        Concatenate[Dataset, P], DataLoader
    ] = DataLoader,
    *args: args,
    **kwargs: kwargs
) -> DataLoader

The val dataloader.

test_dataloader

test_dataloader(
    _dataloader_fn: Callable[
        Concatenate[Dataset, P], DataLoader
    ] = DataLoader,
    *args: args,
    **kwargs: kwargs
) -> DataLoader

The test dataloader.